Seamless integration of air segment in the overall multimodal mobility chain is a key challenge to provide more efficient and sustainable transport services. Technology advances offer a unique opportunity to build a new generation of transport services able to match the evolving expectations and needs of society as a whole. In this context, the passenger-centric approach represents a method to inform the design of future mobility services, supporting quality of life, security and services to citizens traveling across Europe. Relying on the concepts of inclusive design, context of use and task analysis, in this article, we present a comprehensive methodological framework for the analysis of passenger characteristics to elicit features and requirements for future multimodal mobility services, including air leg, that are relevant from the perspective of passengers. The proposed methodology was applied to a series of specific use cases envisaged for three time horizons, 2025, 2035 and 2050, in the context of a European research project. Then, passenger-focused key performance indicators and related metrics were derived to be included in a validation step, with the aim of assessing the extent of benefit for passengers that can be achieved in the forecasted scenarios. The results of the study demonstrate the relevance of human variability in the design of public services, as well as the feasibility of personalized performance assessment of mobility services.
DOCUMENT
Predictive models and decision support toolsallow information sharing, common situational awarenessand real-time collaborative decision-making betweenairports and ground transport stakeholders. To supportthis general goal, IMHOTEP has developed a set of modelsable to anticipate the evolution of an airport’s passengerflows within the day of operations. This is to assess theoperational impact of different management measures onthe airport processes and the ground transport system. Twomodels covering the passenger flows inside the terminal andof passengers accessing and egressing the airport have beenintegrated to provide a holistic view of the passengerjourney from door-to-gate and vice versa.This paper describes IMHOTEP’s application at two casestudy airports, Palma de Mallorca (PMI) and London City(LCY), at Proof of Concept (PoC-level) assessing impactand service improvements for passengers, airport operatorsand other key stakeholders.For the first time onemeasurable process is created to open up opportunities forbetter communication across all associated stakeholders.Ultimately the successful implementation will lead to areduction of the carbon footprint of the passenger journeyby better use of existing facilities and surface transportservices, and the delay or omission of additional airportfacility capacities.
DOCUMENT
Passenger flow management is an important issue at many airports around the world. There are high concentrations of passengers arriving and leaving the airport in waves of large volumes in short periods, particularly in big hubs. This might cause congestion in some locations depending on the layout of the terminal building. With a combination of real airport data, as well as synthetic data obtained through an airport simulator, a Long Short-Term Memory Recurrent Neural Network has been implemented to predict the possible trajectories that passengers may travel within the airport depending on user-defined passenger profiles. The aim of this research is to improve passenger flow predictability and situational awareness to make a more efficient use of the airport, that could also positively impact communication with public and private land transport operators.
DOCUMENT